A norma Devices Profile for Web Services (DPWS) permite a descoberta, a configuração e a interoperabilidade de dispositivos heterogéneos ligados em rede com grande disparidade em termos de capacidade de processamento, desde pequenos eletrodomésticos inteligentes ou controladores em máquinas industriais, até servidores em centros de dados. Os mecanismos de notificação de eventos e de configuração automática previstos pelo DPWS são especialmente adequados a cenários Machine-to-Machine (M2M) devido à sua simplicidade e flexibilidade. No entanto, a escalabilidade em cenários com um elevado número de nós, nomeadamente, em grandes infraestruturas, é limitada. A coerência dos dados armazenados e manipulados pelos mecanismos de autodescoberta também não é adequada a aplicações críticas. Neste artigo, abordamos este problema com uma proposta assente na norma DPWS com os conceitos de difusão epidémica e de consenso, mostrando que é possível compor serviços adequados a diferentes aplicações assegurando garantias de tolerância a faltas e maior escalabilidade.

Availability is an increasingly important quality for today's software-based systems and it has been successfully addressed by the use of closed-loop control systems in self-adaptive systems. Probes are inserted into a running system to obtain information and the information is fed to a controller that, through provided interfaces, acts on the system to alter its behavior. When a failure is detected, pinpointing the source of the failure is a critical step for a repair action. However, information obtained from a running system is commonly incomplete due to probing costs or unavailability of probes. In this paper we address the problem of fault localization in the presence of incomplete system monitoring. We may not be able to directly observe a component but we may be able to infer its health state. We provide formal criteria to determine when health states of unobservable components can be inferred and establish formal theoretical bounds for accuracy when using any spectrum-based fault localization algorithm.

Recent feedback obtained while applying Model-based diagnosis (MBD) in industry suggests that the costs involved in behavioral modeling (both expertise and labor) can outweigh the benefits of MBD as a high-performance diagnosis approach. In this paper, we propose an automatic approach, called ANTARES, that completely avoids behavioral modeling. Decreasing modeling sacrifices diagnostic accuracy, as the size of the ambiguity group (i.e., components which cannot be discriminated because of the lack of information) increases, which in turn increases misdiagnosis penalty. ANTARES further breaks the ambiguity group size by considering the component's false negative rate (FNR), which is estimated using an analytical expression. Furthermore, we study the performance of ANTARES for a number of logic circuits taken from the 74XXX/ISCAS benchmark suite. Our results clearly indicate that sacrificing modeling information degrades the diagnosis quality. However, considering FNR information improves the quality, attaining the diagnostic performance of an MBD approach.

In object oriented software development, automated unit test generation tools typically target one class at a time. A class, however, is usually part of a software project consisting of more than one class, and these are subject to changes over time. This context of a class offers significant potential to improve test generation for individual classes. In this paper, we introduce Continuous Test Generation (CTG), which includes automated unit test generation during continuous integration (i.e., infrastructure that regularly builds and tests software projects). CTG offers several benefits: First, it answers the question of how much time to spend on each class in a project. Second, it helps to decide in which order to test them. Finally, it answers the question of which classes should be subjected to test generation in the first place. We have implemented CTG using the EVOUITE unit test generation tool, and performed experiments using eight of the most popular open source projects available on GitHub, ten randomly selected projects from the SF100 corpus, and five industrial projects. Our experiments demonstrate improvements of up to +58% for branch coverage and up to +69% for thrown undeclared exceptions, while reducing the time spent on test generation by up to +83%.

We present a structure editor that aims to facilitate the presentation and manipulation of handwritten mathematical expressions. The editor is oriented to the calculational mathematics involved in algorithmic problem solving and it provides features that allow reliable structure manipulation of mathematical formulae, as well as flexible and interactive presentations. We describe some of its most important features, including the use of gestures to manipulate algebraic formulae, the structured selection of expressions, definition and redefi- nition of operators in runtime, gesture’s editor, and handwrit- ten templates. The editor is made available in the form of a C# class library which can be easily used to extend existing tools. For example, we have extended Classroom Presenter,a tool for ink-based teaching presentations and classroom interaction. We have tested and evaluated the editor with target users. The results obtained seem to indicate that the software is usable, suitable for its purpose and a valuable contribution to teaching and learning algorithmic problem solving.

Linear logic programming languages have been identified in prior work as viable for specifying stories and analyzing their causal structure. We investigate the use of such a language for specifying story worlds, or settings where generalized narrative actions have uniform effects (not specific to a particular set of characters or setting elements), which may create emergent behavior through feedback loops. We show a sizable example of a story world specified in the language Celf and discuss its interpretation as a story-generating program, a simulation, and an interactive narrative. Further, we show that the causal analysis tools available by virtue of using a proof-theoretic language for specification can assist the author in reasoning about the structure and consequences of emergent stories.

Massive scale data stores, which exhibit highly desirable scalability and availability properties are becoming pivotal systems in nowadays infrastructures. Scalability achieved by these data stores is anchored on data independence; there is no clear relationship between data, and atomic inter-node operations are not a concern. Such assumption over data allows aggressive data partitioning. In particular, data tables are horizontally partitioned and spread across nodes for load balancing. However, in current versions of these data stores, partitioning is either a manual process or automated but simply based on table size. We argue that size based partitioning does not lead to acceptable load balancing as it ignores data access patterns, namely data hotspots. Moreover, manual data partitioning is cumbersome and typically infeasible in large scale scenarios. In this paper we propose an automated table splitting mechanism that takes into account the system workload. We evaluate such mechanism showing that it simple, non-intrusive and efective.